Models as Social Actors in the Diffusion of AI Innovations: A Multilayer, Heterogeneous, Dynamic Network Perspective

Artificial Intelligence (AI) has emerged as a crucial facet of contemporary technological innovation, influencing diverse domains. Consequently, understanding the diffusion and evolution of AI innovations is vital. Scholarly publications have commonly served as proxies for studying these AI innovations. However, previous studies on publication diffusion have largely overlooked the role of models, which is particularly integral for AI innovations as they bridge upstream datasets and downstream applications. Moreover, models form an interdependent network due to their combinational evolution. This paper addresses this gap, examining how the location, movement, and speed of model movement in that model network affect the dissemination of AI research. Using a four-layer network—author collaborations, paper citations, model dependencies, and keyword co-occurrences—we examine 345,383 AI papers from 2000 to 2022. This research aims to contribute to the diffusion of innovation literature and dynamic network analysis, offering several novel insights and advancements

Infrared and Visible Image Fusion Based on Oversampled Graph Filter Banks

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multiscale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviation-based local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications

Spatial Interference: From Coherent To Incoherent

It is well known that direct observation of interference and diffraction pattern in the intensity distribution requires a spatially coherent source. Optical waves emitted from portions beyond the coherence area possess statistically independent phases, and will degrade the interference pattern. In this paper we show an optical interference experiment, which seems contrary to our common knowledge, that the formation of the interference pattern is related to a spatially incoherent light source. Our experimental scheme is very similar to Gabor's original proposal of holography[1], just with an incoherent source replacing the coherent one. In the statistical ensemble of the incoherent source, each sample field produces a sample interference pattern between object wave and reference wave. These patterns completely differ from each other due to the fluctuation of the source field distribution. Surprisingly, the sum of a great number of sample patterns exhibits explicitly an interference pattern, which contains all the information of the object and is equivalent to a hologram in the coherent light case. In this sense our approach would be valuable in holography and other interference techniques for the case where coherent source is unavailable, such as x-ray and electron sources.Comment: 8 pages, 5 figure

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

A Dynamic Analysis of the Complex Interplay Between Internal and External Networks of Open-source Projects on Innovation Behaviors

Open-source community is a dynamic network of heterogeneous projects with fluid boundaries and complex and changing interactions among them. In turn, inside each project is a collaborator network of various developers. As developers move around different projects, the network structure within (internal network) and between projects (external network) change, reciprocally influencing each other. Past research shows that network structure influences innovation behaviors in open-source projects. Yet, no prior studies have examined dynamic interplay between internal and external networks of open-source projects on innovation behaviors. This research will fill in this gap. In particular, we explore changing pattern of external network embeddedness will influence the effect of tie strength of internal collaborator network on innovation behaviors. To answer our research questions, we collect 360,000 projects from GitHub and its related activities in recent two years. We seek to offer several insights flow for the social network field and open-source innovation literature

Data from: Doping induced dielectric anomaly below the Curie temperature in molecular ferroelectric diisopropylammonium bromide

A dielectric anomaly induced by doping has been observed at about 340 K in chlorine doped diisopropylammonium bromide (DIPAB-C). The dielectric anomaly has a switchable behavior, which indicates potential applications on switches and sensors. Temperature dependent Raman spectrum, X-ray diffraction and DSC do not show any anomaly around the dielectric anomaly temperature, which prove that the dielectric anomaly does not come from structure phase transition and has no specific heat variety. It is assumed that this relaxation process can be attributed to the freezing of ferroelectric domain walls induced by the pinning of point defects

dielectric dsc raman xrd

dielectric dsc raman xr

Screen sand retaining precision optimization experiment and a new empirical design model

In the design of sand retaining precision, the existing design methods consider unilateral factors or have limited applicability. A series of experimental tests for screen sand retaining precision optimization were performed. An empirical model was worked out by experiment results fitting. The experimental tests were performed using screen evaluation experimental apparatus with 35 types of formation sand samples and 9 types of screen samples. For all sand samples with different sizes, the optimum sand retaining precisions were determined by calculating the sand-passed ratios and flow capacity index under different sand retaining precisions. The experiment results were fitted, and a new model was put forward to design reasonable screen precision for a given formation sand, considering the median size, the characteristic size of fine composition and uniformity coefficient of the sand and real production condition. The model has been applied in more than 20 wells and the effect is very good. The model considers most important factors affecting sand control effect and has excellent adaptability. And the model provides an effective method to optimize the screen precision without the need of performing lots of experiment tests and is easy to be used. Key words: mechanical screen pipe, sand retaining precision, design optimization, sand retention experiment, sand contro